Empirical Study of Various Classification Techniques for Heart Disease Prediction

It is a known fact that heart acts as one of the most vital as well as complex organ of the body. Without it, majority of living beings will cease to exist. The main function that is performed by the heart is pumping the blood and supplying it to all organs of the whole body, which makes it extremely important. And this is the reason the heart related diseases are extremely sensitive and need to be handled with utmost care. Often it is said that prevention is better than cure. For majority of heart related problems, we often find it after the problem has occurred. But research has shown that around 90% of the cardiovascular diseases can be cured if predicted in advance. From the findings of this study that we have conducted we can find the relation between various factors like age, blood pressure, gender etc. The analysis of these things will help us in developing a deeper understanding of these factors on heart disease. Furthermore, we are modelling the various classification models, KNN, Decision Trees, Logistic Regression, Gaussian Naive Bias, SVM and Random Forests. It is extremely important to predict early on if there is an ailment of heart or not. Heart is the most crucial and sensitive organ of our body and this is a field of critical and crucial work. It provides a medical foresight and helps avid many life-threatening situations. his paper provides an insight of the existing algorithm and it gives an overall summary of the existing work